A New Technique for Database Fragmentation in Distributed Systems

Khan, Shahidul Islam; Hoque, Abu Sayed Md. Latiful

doi:10.5120/940-1318

Cited by 32 publications

(17 citation statements)

References 14 publications

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“…In addition, we assumed that the threshold value of the TTCA Algorithm is equal to the threshold value of the proposed OFRAR Algorithm, which was calculated using Equation 9. In addition, we assumed that the threshold value of the TTCA Algorithm is equal to the threshold value of the proposed OFRAR Algorithm, which was calculated using Equation 9.…”

Section: The Reallocation Decision In Remote Clusters Using Query Dismentioning

confidence: 99%

“…We also assumed that the time interval to take the reallocation decision using the TTCA Algorithm is the same as the time interval used in the proposed OFRAR Algorithm. In addition, we assumed that the threshold value of the TTCA Algorithm is equal to the threshold value of the proposed OFRAR Algorithm, which was calculated using Equation 9. Random N5,N4,N2,N8,N8,N3,N10,N10,N10,N1,N1,N5,N8,N8,N12,N10,N11,N11,N4,N Access Data 1,N1,N3,N10,N10,N9,N12,N7,N2,N5,N3,N7,N5,N6,N6,N10,N2,N1,N2,N2,N2, Sequence 1 N8,N8,N2,N10,N12,N11,N4,N11,N12,N4,N6,N6,N10,N10,N11,N11,N11,N11, N12,N12,N12,N11,N11,N6,N2,N8,N2,N2,N2,N8,N10,N10,N8,N7,N2,N9,N9,N 10,N9,N9,N11,N11,N11,N12,N12,N12,N4,N10,N10,N5,N6,N6,N6,N6,N10,N6, N6,N1,N10,N7,N7,N7,N9,N9,N10,N9,N7,N7,N9,N9,N7,N5,N6,N5,N8,N11,N8, N8,N6,N8,N8,N6,N6,N6,N5,N5,N5,N5,N11,N11,N6,N8,N8,N5,N10,N5,N5,N5, N6,N8,N8,N6,N12,N12,N4,N4,N4,N3,N10,N9,N7,N8 Random N3,N10,N9,N8,N11,N2,N7,N6,N5,N12,N1,N4,N12,N12,N1,N3,N10,N9,N2,N4, Access Data N11,N5,N6,N7,N8,N9,N6,N9,N7,N2,N10,N3,N5,N8,N11,N12,N4,N1,N1,N2,N Sequence 2 11,N7,N3,N4,N10,N8,N1,N5,N6,N12,N12,N9,N9,N1,N10,N8,N10,N10,N9,N5, N5,N6,N5,N6,N7,N8,N10,N8,N8,N2,N2,N2,N2,N6,N6,N6,N6,N11,N5,N11,N1 1,N5,N6,N10,N10,N10,N10,N10,N5,N5,N6,N6,N6,N10,N11,N11,N11,N2,N2,N 5,N6,N7,N8,N8,N8,N8,N8,N9,N10,N9,N10,N10,N7,N6,N2,N11,N11,N11,N11, N11,N8,N8,N8,N12,N12,N4,N4,N4,N6,N6,N6,N6,N2,N5,N5,N12,N12,N12,N1 2,N9,N8,N8,N5,N4,N11,N8,N7,N7,N7,N10,N10,N10 Random N1,N1,N3,N10,N10,N9,N12,N12,N12,N4,N4,N2,N8,N8,N8,N8,N7,N5,N6,N6, Access Data N2,N1,N3,N10,N10,N10,N1,N1,N12,N10,N11,N11,N4,N4,N4,N3,N10,N9,N7, Sequence 3 N8,N7,N2,N5,N3,N2,N2,N2,N8,N8,N8,N2,N12,N11,N4,N11,N12,N4,N6,N6,N 10,N10,N10,N10,N11,N11,N11,N11,N12,N12,N12,N11,N11,N6,N2,N2,N2,N2, N8,N8,N8,N8,N8,N10,N10,N10,N10,N8,N7,N2,N9,N9,N9,N9,N11,N11,N11,N 12,N12,N12,N4,N10,N10,N5,N6,N6,N6,N6,N10,N6,N6,N1,N10,N7,N7,N7,N9, N9,N10,N9,N7,N7,N9,N9,N7,N5,N6,N5,N8,N11,N8,N8,N6,N6,N6,N6,N5,N5, N5,N5,N11,N11,N6,N8,N8,N5,N5,N5,N5,N5,N5,N6,N6…”

Section: The Reallocation Decision In Remote Clusters Using Query Dismentioning

confidence: 99%

See 1 more Smart Citation

Dynamic data reallocation and replication over a cloud environment

Raouf

Badr

Tolba

2018

Concurrency and Computation

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The performance and efficiency of the distributed database system (DDBS) design are dependent on proper data fragmentation, reallocation, and replication of global relations. In this research, a Cluster Based Distributed and Parallel Database System (CB-DDBS) architecture over a cloud environment is proposed. The proposed CB-DDBS architecture processes the client's query requests, which access the clustered DDBS from anywhere. It also allows vertical and horizontal fragmentation, allocation, and replication decisions to be taken statically at the initial stage of the design. In addition, it allows migration and/or replication decisions to be taken by each cluster independently of other clusters using the proposed Optimal Fragment Reallocation and Replication (OFRAR) Algorithm. The proposed CB-DDBS architecture and the proposed OFRAR Algorithm are tested in both Amazon cloud environment and a simulated environment. Experimental results show that the proposed OFRAR Algorithm efficiently reduces the communication costs for typical access patterns, the overloads of the sites, and the frequency and time spent on fragment migration over the network sites by imposing a stricter condition for fragment reallocation and replication, resulting in a great overall improvement in the DDBS performance. It also shows that the proposed CB-DDBS architecture results in a significant reduction of the execution time needed for the transactions and for noticing an error.

show abstract

Section: The Reallocation Decision In Remote Clusters Using Query Dismentioning

confidence: 99%

Section: The Reallocation Decision In Remote Clusters Using Query Dismentioning

confidence: 99%

Dynamic data reallocation and replication over a cloud environment

Raouf

Badr

Tolba

2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In [4], the authors provide a solution for initial fragmentation problem of relational database for DDBMS. It partitioned the relational database properly at the initial stage when no data statistics and query execution frequencies are available.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Database Fragmentation and Allocation to Improve the Distributed Database Management System Performance

Al-Sayyed¹,

Zaghoul²,

Suleiman³

et al. 2014

JSEA

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The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.

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“…To the best of our knowledge, such technique has not been proposed for vertical fragmentation. Our proposed work is inspired with the recent work done by Khan in [12]. Chang developed a network flow algorithm [13] to solve the issue of database allocation.…”

Section: Related Workmentioning

confidence: 99%

“…Khan et al proposed a new technique for vertical fragmentation using CRUD matrix. In his technique, he gave a noble idea of database design without the availability of empirical data [12]. To the best of our knowledge, such technique has not been proposed for vertical fragmentation.…”

Section: Related Workmentioning

confidence: 99%

An Apriori-Based Vertical Fragmentation Technique for Heterogeneous Distributed Database Transactions

Ramesh

Kumar

et al. 2014

Intelligent Computing, Networking, and Informatics

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Many distributed applications have become very useful in these days, such as reservation enquiry in railways and airways, status of postal, and Internet banking. All these are related to common database, through which they are connected to provide the result of user's query. But, the purpose of the above-distributed application will fail if we do not work cleverly on distributed database design. If we design the database with some motive to improve the performance of the application, the tolerance of the distributed database increases and it can handle several queries concurrently. So, improving the performance of a database system is a challenging research area. Earlier approaches have suggested fragmentation solution based on the data access and frequency of the queries. Previously distributed design used attribute matrix approach to generate a new matrix upon which they applied to their clustering algorithm to find out different fragments, which involves little bit complicated computation. In this paper, we present a vertical fragmentation technique, which uses attribute usage matrix instead of attribute affinity matrix by applying apriori algorithm on usage matrix for partitioning the relations. Our result comprises the proposed technique, which reduces the overhead of complicated computations.

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A New Technique for Database Fragmentation in Distributed Systems

Cited by 32 publications

References 14 publications

Dynamic data reallocation and replication over a cloud environment

Dynamic data reallocation and replication over a cloud environment

A New Approach for Database Fragmentation and Allocation to Improve the Distributed Database Management System Performance

An Apriori-Based Vertical Fragmentation Technique for Heterogeneous Distributed Database Transactions

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